In spite of improvements in detection, treatment, and prevention techniques, dental caries remain a widely prevalent condition affecting people of all age groups. If not properly and promptly treated, caries can lead to permanent tooth damage and even to loss of teeth.
Traditional methods for caries detection include visual examination and tactile probing with a sharp dental explorer device, often assisted by radiographic (x-ray) imaging. Detection using these methods can be somewhat subjective, varying in accuracy due to many factors, including practitioner expertise, location of the infected site, extent of infection, viewing conditions, accuracy and sensitivity of x-ray equipment and processing, and other factors. There are also hazards associated with conventional detection techniques, including the risk of damaging weakened teeth and spreading infection with tactile methods as well as exposure to x-ray radiation. By the time caries are evident under visual and tactile examination, the disease is generally in an advanced stage, requiring a filling and, if not timely treated, possibly leading to tooth loss.
In response to the need for improved caries detection methods, there has been considerable interest in improved imaging techniques that do not employ x-rays. In particular, it has been shown that certain optical responses differ between sound and carious tooth regions. Difference in light scattering properties causes reflectance of light from the illuminated tooth area to be at measurably different levels for normal versus carious areas. This effect can be used to help identify tooth regions with early caries, which tend to appear brighter than surrounding sound structures in images captured by reflectance imaging devices, such as an intraoral camera.
Another optical method that has been employed for caries detection is based on tooth fluorescence, emitted when teeth are illuminated with high intensity ultraviolet-blue light. This technique operates on the principle that sound, healthy tooth enamel yields a higher, predominately green fluorescence intensity under excitation from some wavelengths than does de-mineralized enamel that has been damaged by caries infection. The strong correlation between mineral loss and loss of green fluorescence for ultraviolet-blue light excitation is then used to identify and assess carious areas of the tooth. A different relationship has been found for red fluorescence, a region of the spectrum for which bacteria and bacterial by-products in carious regions fluoresce more pronouncedly than do healthy areas.
Among commercialized products for dental imaging using green fluorescence behavior is the Quantitative Light Fluorescence (QLF) Clinical System from Inspektor Research Systems BV, Amsterdam, The Netherlands. Using a different approach, the Diagnodent Laser Caries Detection Aid from KaVo Dental Corporation, Lake Zurich, Ill., detects caries activity by monitoring the intensity of red fluorescence of bacterial by-products under illumination from red light.
In the related U.S. patent applications cited earlier, another optical technique known as fluorescence imaging with reflectance enhancement (FIRE) has been described for caries detection. In the FIRE method described in these applications, both reflectance and fluorescence imaging effects are combined, enabling caries to be detected with higher contrast from surrounding sound tooth structures.
One problem that is common to existing dental imaging systems relates to the delay period between the time that the tooth is initially being screened and the image of the tooth is obtained and the time a possible caries condition is identified or reported to the dentist or technician. With existing systems, tooth screening (during which the images are obtained) and caries detection (during which the images are processed and analyzed to identify carious regions) are carried out as two separate steps. In practice, at an appropriate point during screening, a still image capture is first obtained from the tooth in response to an operator instruction. Then, in a subsequent step, the image data are processed and analyzed for carious conditions to provide the clinician with a processed image (possibly also accompanied by a report) indicating caries information, such as apparent location, size, and severity, for example. This caries information is available only after the conclusion of the tooth screening step and only after image processing/analysis steps are completed.
When the caries information becomes available at this later time after screening, the dentist often needs to go back and re-examine the imaged tooth in order to look more closely at the reported problem area. This delay is inconvenient and lengthens the duration of the examination session. It can be appreciated that there would be an advantage to an apparatus that would provide more immediate feedback to the examining practitioner, so that problem areas can be identified and examined more closely at the time of screening. However, this advantage is not available with conventional systems, due to factors such as the difficulty of detection, the intensive computation requirements needed for many existing detection methods, and the amount of image data that is required for each tooth.
In spite of some advancements, an acknowledged problem with real-time detection for existing dental imaging systems relates to the difficulty of identifying caries in teeth images without extensive image processing or absent a highly skilled practitioner who is familiar with this specialized equipment. Systems such as the QLF system described earlier may show real-time fluorescence images, but these displayed images are generally only of value to the experienced clinician who is trained in interpreting the displayed image from tooth fluorescence in order to identify a caries area. In general, caries detection from tooth images, whether using white light or fluorescence images, requires a relatively high level of expertise from the practitioner. Auto-detection by computer-aided image analysis can eliminate the expertise requirement. However, because current auto-detection algorithms usually involve time-consuming image processing; they are not suitable for real time identification of caries.
It can be appreciated that there would be advantages to a method of image processing that can quickly identify carious areas from teeth images to provide immediate feedback of information suggestive of carious conditions. Such a method would allow auto-detection of caries in real time that would be useful even for the novice or relatively untrained user.